Spatially exploring the intersection of socioeconomic status and Canadian cancer-related medical crowdfunding campaigns
Bibliographic record
Abstract
OBJECTIVES: Medical crowdfunding is a rapidly growing practice where individuals leverage social networks to raise money for health-related needs. This practice has allowed many to access healthcare and avoid medical debt but has also raised a number of ethical concerns. A dominant criticism of this practice is that it is likely to increase inequities in access to healthcare if persons from relatively wealthy backgrounds, media connections, tech-savvy and educational attainments are best positioned to use and succeed with crowdfunding. However, limited data has been published to support this claim. Our objective in this paper is to assess this concern using socioeconomic data and information from crowdfunding campaigns. SETTING: To assess this concern, we present an exploratory spatial analysis of a new dataset of crowdfunding campaigns for cancer-related care by Canadian residents. PARTICIPANTS: Four datasets were used: (1) a medical crowdfunding dataset that included cancer-related campaigns posted by Canadians, (2) 2016 Census Profile for aggregate dissemination areas, (3) aggregate dissemination area boundaries and (4) forward sortation area boundaries. RESULTS: Our exploratory spatial analysis demonstrates that use of crowdfunding for cancer-related needs in Canada corresponds with high income, home ownership and high educational attainment. Campaigns were also commonly located near city centres. CONCLUSIONS: These findings support concerns that those in positions of relative socioeconomic privilege disproportionately use crowdfunding to address health-related needs. This study was not able to determine whether other socioeconomic dimensions such as race, gender, ethnicity, nationality and linguistic fluency are also correlated with use of medical crowdfunding. Thus, we call for further research to explore the relationship between socioeconomic variables and medical crowdfunding campaigning to explore these other socioeconomic variables and campaigns for needs unrelated to cancer.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".